Empowering people to make well-informed decisions has become increasingly important in today's fast paced environment. Providing individuals with relevant and timely information is an essential element in facilitating such well-informed decisions. However, certain information that is noise to some may be valuable to others. Additionally, some information can also be temporally critical and as such there may be significant value associated with timely delivery of such information. Moreover, with the growth of computer and information systems, and related network technologies such as wireless and Internet communications, ever increasing amounts of electronic information are communicated, transferred and subsequently processed by users and/or systems. As an example, web browsers have become a popular application amongst computer users for generating and receiving content. With the advent of the Internet, for instance, exchanging content (e.g., messages, files, web pages, etc.) has become an important factor influencing why many people acquire computers. Nevertheless, with the heightened popularity of web browsers and other information transfer systems, problems have begun to appear with regard to managing, processing, and rendering increasing amounts of content.
There are many applications for automatic classification of items such as email, documents, images, and recordings. To address this need, a plethora of classifiers have been developed based, for example, on probabilistic dependency models learned from training data. Examples of such models can include logistic regression models, decision tree models, support vector machines, neural networks, Naïve Bayes, and the like.
Naïve Bayes classifiers to date have been one of the most widely utilized classifiers ever developed in the text domain even though the classifier is generally recognized as providing solutions that are just “good enough”. Nevertheless, Naïve Bayes classifiers are utilized by a plethora of classification applications, typically to provide a lower bound for the classification while the upper classification bounds are generally handled by more arcane and abstruse methodologies despite the fact that utilization of such techniques in some cases ekes out only marginal gains in terms of cost and time over utilization of the ubiquitous Naïve Bayes classifier, while in other instances gains accrued can be dependent on factors such as document representation and precision requirements (e.g., if high precision is not required, many standard versions of Naïve Bayes classifiers can perform adequately.